An agent that uses multiple MCP servers (Tavily search, YouTube transcript, math operations, weather) integrated with LangGraph and LangChain to answer queries dynamically
This server exhibits critical quality gaps across naming, descriptions, and schema completeness. Of 5 tools, 2 lack any description at all (add, multiply), and 3 have minimal parameter documentation. Input schemas are present but severely underdocumented, no parameters have descriptions in the schema definitions, violating the baseline that 100% of A+ tool params have descriptions. Tool naming is inconsistent: 'add' and 'multiply' lack verbs that convey action, while 'get_tavily_results' and 'get_youtube_transcript' follow better patterns. Output schemas are completely undocumented, the LLM has no explicit guidance on what fields to expect from any response. Error handling is minimal: search_tavily returns an error dict, but recovery guidance is absent. No tool declares whether it modifies state, preventing LLMs from reasoning about idempotence and retry safety. The math tools expose no descriptions at all, forcing LLMs to guess intent from the function name alone.
Fetches Tavily search results for a given query.
Get weather for location.
Fetches transcript from a given YouTube URL.
All five tools have zero parameter descriptions in their input schemas. The rubric baseline is 100% of A+ tool params have descriptions. Parameters like 'a', 'b', 'query', 'location', 'url' are ambiguous without context. LLMs cannot infer what values to pass.
No tool documents its output schema. The rubric requires documentation of return types so LLMs know what fields to expect and can plan downstream calls. All tools return JSON objects but structure is undocumented.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 33 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 26 | - | v1 |
Naming inconsistency: 'add' and 'multiply' do not start with action verbs. These should be named 'perform_add' or 'calculate_add' and 'perform_multiply' or 'calculate_multiply' to be explicit about their function.
Error handling lacks recovery guidance. get_tavily_results returns {"error": "Tavily API key is missing..."} and get_youtube_transcript returns {"error": "Invalid YouTube URL"}, but neither tells the LLM what to do next. Per pattern:recovery-guide, errors must guide the agent on retry/fallback options.
No tool declares state modifications. Tools like get_tavily_results call external APIs and are read-only, but descriptions do not state this. LLMs cannot reason about idempotence, retry safety, or side effects.
Parameter 'query' in get_tavily_results has no constraints, format hints, or examples in the description.